Abstract
Wind erosion is one of the most severe environmental problems in arid, semiarid, and dry sub-humid regions of the planet. This paper aimed to identify areas sensitive to wind erosion in Northeastern Algeria (Wilaya of Tebessa) based on empirical model using analytic hierarchy process, fuzzy analytic hierarchy process approaches, and geomatics-based techniques. Sixteen causative factors were used incorporating meteorological, soil erodibility, physical environment, and anthropogenic impacts as main available inputs in this approach. Weighted linear combination algorithm was adopted to combine all standardized raster layers. Area under curve value equal to 0.96 indicates an excellent accuracy for the proposed approach. Globally, wind erosion risk increases gradually from the North to South of the whole area. Besides, it was found that areas with slight, moderate, high, and very high risk covered 9.65%, 25.83%, 24.30%, and 40.22% of the total area, respectively. Our results highlighted the potential of additive linear model and free available medium resolution multi-source remote sensing data in studying natural hazards and disasters mainly under data-scarce or areas of difficult access in developing countries. In addition, restoration and re-vegetation activities of sensitive areas at high risk of wind erosion represent a challenge for researchers and decision-makers.
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Acknowledgements
The authors deeply acknowledge the help of the directorate general of forest, directorate general for agriculture, high commission for the development of steppe for providing the necessary government statistical data of Tebessa region, and the Ministry of High Education and Scientific Research of Algeria (PRFU Project D01N01UN120120210001to Dr. M. Boukoucha). Besides, the authors are grateful to anonymous reviewers for their valuable comments and suggestions greatly improved quality of the manuscript.
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Mihi, A., Benaradj, A. Assessing and mapping wind erosion-prone areas in Northeastern Algeria using additive linear model, fuzzy logic, multicriteria, GIS, and remote sensing. Environ Earth Sci 81, 47 (2022). https://doi.org/10.1007/s12665-021-10154-2
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DOI: https://doi.org/10.1007/s12665-021-10154-2